Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python

4 (1)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every field you can think of from medicine, to defense, to farming. It is an approach to use statistical analysis and other informational engineering to data which has a geographical or geospatial aspect. And this typically involves applications capable of geospatial display and processing to get a compiled and useful data. "Learning Geospatial Analysis with Python" uses the expressive and powerful Python programming language to guide you through geographic information systems, remote sensing, topography, and more. It explains how to use a framework in order to approach Geospatial analysis effectively, but on your own terms. "Learning Geospatial Analysis with Python" starts with a background of the field, a survey of the techniques and technology used, and then splits the field into its component speciality areas: GIS, remote sensing, elevation data, advanced modelling, and real-time data. This book will teach you everything there is to know, from using a particular software package or API to using generic algorithms that can be applied to Geospatial analysis. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. "Learning Geospatial Analysis with Python" will round out your technical library with handy recipes and a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python
About the Author
About the Reviewers

Chapter 6. Python and Remote Sensing

In this chapter, we will discuss Remote Sensing. This field grows more exciting every day as more satellites are launched and the distribution of data becomes easier. The high availability of satellite and aerial images, as well as interesting new types of sensors launching each year is changing the role remote sensing plays in understanding our world.

And in this field, Python is quite capable. However, in this chapter we will rely more on Python bindings to C libraries than we have in the previous chapters, where the focus was more on using pure Python. The only reason for this change is the size and complexity of remotely sensed data. In remote sensing, we step through each pixel in an image and perform some form of query or mathematical process. An image can be thought of as a large numerical array. And in remote sensing these arrays can be quite large on the order of tens of megabytes to several gigabytes. While Python is fast, only C-based libraries can provide the speed needed to loop through arrays at a tolerable speed.

The compromise that we make in this chapter is that whenever possible we'll use the Python Imaging Library (PIL) for image processing and NumPy which provides multi-dimensional array mathematics. While written in C for speed, these libraries are designed for Python and provide a pythonic API.

In this chapter we'll start with basic image manipulation and build on each exercise all the way to automatic change detection. Here are the topics we'll cover:

  • Swapping image bands

  • Creating image histograms

  • Classifying images

  • Extracting features from images

  • Change detection